Examine age-related change in parameter estimates from models
Run regressions between model parameters and age
##
## Call:
## lm(formula = LL ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -141.012 -41.027 -2.953 37.260 141.220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -265.726 25.786 -10.305 < 2e-16 ***
## age 3.982 1.386 2.873 0.00508 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 62.96 on 90 degrees of freedom
## Multiple R-squared: 0.08399, Adjusted R-squared: 0.07382
## F-statistic: 8.253 on 1 and 90 DF, p-value: 0.005075
##
## Call:
## lm(formula = alphaPosChoice ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2864 -0.1951 -0.1085 0.1003 0.7838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.123105 0.116696 1.055 0.294
## age 0.006673 0.006273 1.064 0.290
##
## Residual standard error: 0.2849 on 90 degrees of freedom
## Multiple R-squared: 0.01242, Adjusted R-squared: 0.001445
## F-statistic: 1.132 on 1 and 90 DF, p-value: 0.2903
##
## Call:
## lm(formula = alphaNegChoice ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15491 -0.12216 -0.07414 -0.01518 0.86539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.219194 0.095211 2.302 0.0236 *
## age -0.005970 0.005118 -1.166 0.2465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2325 on 90 degrees of freedom
## Multiple R-squared: 0.01489, Adjusted R-squared: 0.003948
## F-statistic: 1.361 on 1 and 90 DF, p-value: 0.2465
##
## Call:
## lm(formula = alphaPosComp ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17039 -0.14045 -0.11397 -0.00871 0.82969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.207457 0.100743 2.059 0.0424 *
## age -0.003459 0.005415 -0.639 0.5246
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.246 on 90 degrees of freedom
## Multiple R-squared: 0.004514, Adjusted R-squared: -0.006547
## F-statistic: 0.4081 on 1 and 90 DF, p-value: 0.5246
##
## Call:
## lm(formula = alphaNegComp ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.18426 -0.18099 -0.15342 0.05683 0.80339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1787245 0.1175399 1.521 0.132
## age 0.0002398 0.0063179 0.038 0.970
##
## Residual standard error: 0.287 on 90 degrees of freedom
## Multiple R-squared: 1.6e-05, Adjusted R-squared: -0.01109
## F-statistic: 0.00144 on 1 and 90 DF, p-value: 0.9698
##
## Call:
## lm(formula = betaAgency ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3948 -3.7399 -0.5663 2.5973 18.5924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0129 2.2376 1.793 0.0763 .
## age 0.2955 0.1203 2.457 0.0159 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.463 on 90 degrees of freedom
## Multiple R-squared: 0.06287, Adjusted R-squared: 0.05246
## F-statistic: 6.038 on 1 and 90 DF, p-value: 0.01592
##
## Call:
## lm(formula = betaMachine ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.8618 -3.3465 -0.7262 2.3170 16.5670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4853 2.0354 2.204 0.0301 *
## age 0.1644 0.1094 1.502 0.1365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.97 on 90 degrees of freedom
## Multiple R-squared: 0.02446, Adjusted R-squared: 0.01362
## F-statistic: 2.257 on 1 and 90 DF, p-value: 0.1365
##
## Call:
## lm(formula = agencyBonus ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.84601 -0.16303 -0.04726 0.04737 1.75107
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.123635 0.171281 0.722 0.472
## age 0.010736 0.009207 1.166 0.247
##
## Residual standard error: 0.4182 on 90 degrees of freedom
## Multiple R-squared: 0.01488, Adjusted R-squared: 0.003939
## F-statistic: 1.36 on 1 and 90 DF, p-value: 0.2466
Learning rate model
## Mixed Model Anova Table (Type 3 tests, S-method)
##
## Model: estimate ~ ageZ * valence * agency + (1 | subID)
## Data: learning_rates
## Effect df F p.value
## 1 ageZ 1, 90.00 0.04 .842
## 2 valence 1, 270.00 3.07 + .081
## 3 agency 1, 270.00 0.25 .617
## 4 ageZ:valence 1, 270.00 0.63 .428
## 5 ageZ:agency 1, 270.00 0.12 .728
## 6 valence:agency 1, 270.00 10.05 ** .002
## 7 ageZ:valence:agency 1, 270.00 2.10 .148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: estimate ~ ageZ * valence * agency + (1 | subID)
## Data: data
##
## REML criterion at convergence: 107.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1984 -0.5828 -0.3909 0.0827 3.2210
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.004016 0.06337
## Residual 0.065509 0.25595
## Number of obs: 368, groups: subID, 92
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.170801 0.014889 90.000000 11.472 <2e-16 ***
## ageZ -0.002984 0.014909 90.000000 -0.200 0.8418
## valence1 -0.023387 0.013342 269.999999 -1.753 0.0808 .
## agency1 0.006673 0.013342 269.999999 0.500 0.6174
## ageZ:valence1 -0.010603 0.013360 269.999999 -0.794 0.4281
## ageZ:agency1 0.004651 0.013360 269.999999 0.348 0.7280
## valence1:agency1 -0.042297 0.013342 269.999999 -3.170 0.0017 **
## ageZ:valence1:agency1 -0.019375 0.013360 269.999999 -1.450 0.1482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ageZ valnc1 agncy1 agZ:v1 agZ:g1 vln1:1
## ageZ 0.000
## valence1 0.000 0.000
## agency1 0.000 0.000 0.000
## ageZ:valnc1 0.000 0.000 0.000 0.000
## ageZ:agncy1 0.000 0.000 0.000 0.000 0.000
## vlnc1:gncy1 0.000 0.000 0.000 0.000 0.000 0.000
## agZ:vlnc1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
##
## Paired t-test
##
## data: model_params$alphaPosChoice and model_params$alphaNegChoice
## t = 3.2464, df = 91, p-value = 0.001636
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 0.05098873 0.21174803
## sample estimates:
## mean difference
## 0.1313684
##
## Paired t-test
##
## data: model_params$alphaPosComp and model_params$alphaNegComp
## t = -0.8713, df = 91, p-value = 0.3859
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.12404217 0.04840164
## sample estimates:
## mean difference
## -0.03782026
Plot relations between model parameters and age

Parameter summary statistics
Questionnaire relations
DOC
##
## Call:
## lm(formula = DOC ~ zAge, data = DOC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.234 -6.388 -0.270 7.449 30.317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 95.527 1.255 76.11 <2e-16 ***
## zAge 2.446 1.274 1.92 0.058 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.97 on 89 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03978, Adjusted R-squared: 0.02899
## F-statistic: 3.687 on 1 and 89 DF, p-value: 0.05804
##
## Call:
## lm(formula = DOC ~ zBetaAgency * zAgencyBonus * zAge, data = DOC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.318 -6.665 -0.614 7.416 30.307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 95.4327 1.4180 67.300 <2e-16 ***
## zBetaAgency -0.6607 1.5865 -0.416 0.6782
## zAgencyBonus 1.3423 3.6437 0.368 0.7135
## zAge 1.9834 1.4240 1.393 0.1674
## zBetaAgency:zAgencyBonus 0.9452 2.7639 0.342 0.7332
## zBetaAgency:zAge 0.7317 1.4672 0.499 0.6193
## zAgencyBonus:zAge -6.2496 3.4316 -1.821 0.0722 .
## zBetaAgency:zAgencyBonus:zAge -3.1118 2.5602 -1.215 0.2276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.95 on 83 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1082, Adjusted R-squared: 0.03295
## F-statistic: 1.438 on 7 and 83 DF, p-value: 0.2013
LOC
##
## Call:
## lm(formula = LOC ~ zAge, data = LOC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.4335 -3.3923 -0.4242 3.4805 10.1914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.6288 0.4372 28.886 <2e-16 ***
## zAge 0.2453 0.4392 0.559 0.578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.17 on 89 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.003494, Adjusted R-squared: -0.007703
## F-statistic: 0.3121 on 1 and 89 DF, p-value: 0.5778
##
## Call:
## lm(formula = LOC ~ zBetaAgency * zAgencyBonus * zAge, data = LOC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.9583 -3.4626 -0.3911 3.2787 10.0915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.92475 0.50467 25.610 <2e-16 ***
## zBetaAgency -0.14273 0.56284 -0.254 0.800
## zAgencyBonus 1.41264 1.29225 1.093 0.277
## zAge 0.36724 0.50171 0.732 0.466
## zBetaAgency:zAgencyBonus 1.23889 0.98011 1.264 0.210
## zBetaAgency:zAge -0.02981 0.52015 -0.057 0.954
## zAgencyBonus:zAge -1.01683 1.21653 -0.836 0.406
## zBetaAgency:zAgencyBonus:zAge -0.31812 0.90795 -0.350 0.727
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.238 on 83 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.04037, Adjusted R-squared: -0.04056
## F-statistic: 0.4988 on 7 and 83 DF, p-value: 0.8329
BDI
##
## Call:
## lm(formula = zBDI ~ zAge, data = BDI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.94728 -0.78671 -0.01517 0.72806 2.78555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.122e-16 1.042e-01 0.000 1.000
## zAge 3.587e-02 1.048e-01 0.342 0.733
##
## Residual standard error: 0.9993 on 90 degrees of freedom
## Multiple R-squared: 0.001301, Adjusted R-squared: -0.009796
## F-statistic: 0.1172 on 1 and 90 DF, p-value: 0.7329
##
## Call:
## lm(formula = zBDI ~ zBetaAgency * zAgencyBonus * zAge, data = BDI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.85715 -0.65938 0.00033 0.69522 2.67871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08489 0.11978 0.709 0.480
## zBetaAgency 0.20766 0.13439 1.545 0.126
## zAgencyBonus 0.34329 0.30858 1.113 0.269
## zAge 0.04168 0.11917 0.350 0.727
## zBetaAgency:zAgencyBonus 0.28280 0.23403 1.208 0.230
## zBetaAgency:zAge -0.02180 0.12418 -0.176 0.861
## zAgencyBonus:zAge 0.07767 0.29050 0.267 0.790
## zBetaAgency:zAgencyBonus:zAge 0.11565 0.21679 0.533 0.595
##
## Residual standard error: 1.012 on 84 degrees of freedom
## Multiple R-squared: 0.04419, Adjusted R-squared: -0.03546
## F-statistic: 0.5549 on 7 and 84 DF, p-value: 0.7903
STAI
##
## Call:
## lm(formula = zSTAI_t ~ zAge, data = STAI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.82245 -0.96538 0.01261 0.83118 2.16747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001085 0.104658 0.010 0.992
## zAge 0.060134 0.106243 0.566 0.573
##
## Residual standard error: 0.9982 on 89 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.003587, Adjusted R-squared: -0.007609
## F-statistic: 0.3204 on 1 and 89 DF, p-value: 0.5728
##
## Call:
## lm(formula = zSTAI_t ~ zBetaAgency * zAgencyBonus * zAge, data = STAI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.84138 -0.92206 -0.05708 0.79479 2.23553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03420 0.12090 0.283 0.778
## zBetaAgency 0.15007 0.13569 1.106 0.272
## zAgencyBonus 0.18969 0.31114 0.610 0.544
## zAge 0.02867 0.12130 0.236 0.814
## zBetaAgency:zAgencyBonus 0.16540 0.23606 0.701 0.485
## zBetaAgency:zAge -0.08046 0.12645 -0.636 0.526
## zAgencyBonus:zAge -0.05753 0.29196 -0.197 0.844
## zBetaAgency:zAgencyBonus:zAge -0.09840 0.21793 -0.452 0.653
##
## Residual standard error: 1.016 on 83 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03663, Adjusted R-squared: -0.04462
## F-statistic: 0.4508 on 7 and 83 DF, p-value: 0.867
##
## Call:
## lm(formula = zSTAI_s ~ zAge, data = STAI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9203 -0.6732 -0.1498 0.4769 3.1426
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.937e-16 1.033e-01 0.000 1.000
## zAge 1.368e-01 1.038e-01 1.318 0.191
##
## Residual standard error: 0.9905 on 90 degrees of freedom
## Multiple R-squared: 0.01894, Adjusted R-squared: 0.008035
## F-statistic: 1.737 on 1 and 90 DF, p-value: 0.1909
##
## Call:
## lm(formula = zSTAI_s ~ zBetaAgency * zAgencyBonus * zAge, data = STAI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9560 -0.6394 -0.1377 0.5929 3.2736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10180 0.11729 0.868 0.388
## zBetaAgency 0.18433 0.13159 1.401 0.165
## zAgencyBonus 0.34019 0.30216 1.126 0.263
## zAge 0.14574 0.11669 1.249 0.215
## zBetaAgency:zAgencyBonus 0.36647 0.22917 1.599 0.114
## zBetaAgency:zAge -0.09988 0.12160 -0.821 0.414
## zAgencyBonus:zAge -0.07798 0.28446 -0.274 0.785
## zBetaAgency:zAgencyBonus:zAge -0.02332 0.21228 -0.110 0.913
##
## Residual standard error: 0.9909 on 84 degrees of freedom
## Multiple R-squared: 0.08352, Adjusted R-squared: 0.007147
## F-statistic: 1.094 on 7 and 84 DF, p-value: 0.3748
---
title: "VoC Analyses Part 3: Analyze Reinforcement-Learning Results"
date: 1/8/24
output:
    html_document:
        df_print: 'paged'
        toc: true
        toc_float:
            collapsed: false
            smooth_scroll: true
        number_sections: false
        code_download: true
        self_contained: true
---

```{r chunk settings, include = FALSE}
# set chunk settings
knitr::opts_chunk$set(echo = FALSE, 
                      cache = TRUE,
                      message = FALSE,
                      warning = FALSE)
knitr::opts_chunk$set(dpi=600)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```

# Load packages 
```{r load packages, include = F}

# list all packages required for the analysis
list.of.packages <- c("tidyverse", "latex2exp", "afex")

# check if all packages are installed, if not, install them.
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

# load all packages 
lapply(list.of.packages, library, character.only = TRUE)

# add theme for plotting
voc_theme <- function () {
  theme(
    panel.border = element_rect(fill = "transparent", color="gray75"),
    panel.background  = element_blank(),
    plot.background = element_blank(), 
    legend.background = element_rect(fill="transparent", colour=NA),
    legend.key = element_rect(fill="transparent", colour=NA),
    line = element_blank(),
    axis.ticks = element_line(color="gray75"),
    text=element_text(family="Avenir"),
    axis.text = element_text(size = 12),
    axis.title = element_text(size = 15),
    title = element_text(size = 15),
    strip.background = element_blank(),
    strip.text = element_text(size=12)
  )
}

color8 = "#80dbb2"
color1 = "#00b4d8"
color2 = "#0077b6"
color3 = "#03045e"
color4 = "#84347C"
color5 = "#B40424"
color6 = "#EB6D1E"
color7 = "#f5b68f"

scale_this <- function(x){
  (x - mean(x, na.rm=TRUE)) / sd(x, na.rm=TRUE)
}

```

## Load data  
```{r, load data}
#load data
aics = read_csv("RL_modeling/output/aics_all_16_models_100iter.csv")
```

```{r pivot AIC data longer}
aics1 <- pivot_longer(aics, 
                cols = oneAlpha_oneBeta:fourAlpha_twoBeta_agencyBonus,
                names_to = "model",
                values_to = "AIC")
```


#  AIC average by age group 
```{r plot AIC by age group, fig.width = 8, fig.height = 5, units = "in"}

# Add id and other demographic info
sub_info <- read_csv('data/voc_sub_info.csv') %>%
    mutate(age_group = case_when(age < 13 ~ "Children",
                                 age > 12.99 & age < 18 ~ "Adolescents",
                                 age > 17.99 ~ "Adults"))

sub_info$age_group <- factor(sub_info$age_group, levels = c("Children", "Adolescents", "Adults"))

model_results <- full_join(sub_info, aics1, by = c("subID"))

model_results$model <- factor(model_results$model, 
                              levels = c("oneAlpha_oneBeta",
                                         "oneAlpha_twoBeta",
                                         "twoAlpha_oneBeta",
                                         "twoAlpha_twoBeta",
                                         "twoAlphaValenced_oneBeta",
                                         "twoAlphaValenced_twoBeta",
                                         "fourAlpha_oneBeta",
                                         "fourAlpha_twoBeta",
                                         "oneAlpha_oneBeta_agencyBonus",
                                         "oneAlpha_twoBeta_agencyBonus",
                                         "twoAlpha_oneBeta_agencyBonus",
                                         "twoAlpha_twoBeta_agencyBonus",
                                         "twoAlphaValenced_oneBeta_agencyBonus",
                                         "twoAlphaValenced_twoBeta_agencyBonus",
                                         "fourAlpha_oneBeta_agencyBonus",
                                         "fourAlpha_twoBeta_agencyBonus"))
model_results <- model_results %>%
    mutate(agencyBonus = case_when(str_detect(model, "agency") ~ "With Agency Bonus",
                                  !str_detect(model, "agency") ~ "No Agency Bonus"),
           shortName = str_remove(model, '_agencyBonus'))

model_results$shortName <- factor(model_results$shortName,
                                  levels = c("oneAlpha_oneBeta",
                                         "oneAlpha_twoBeta",
                                         "twoAlpha_oneBeta",
                                         "twoAlpha_twoBeta",
                                         "twoAlphaValenced_oneBeta",
                                         "twoAlphaValenced_twoBeta",
                                         "fourAlpha_oneBeta",
                                         "fourAlpha_twoBeta"))
                                 
#summarize
model_summary <- model_results %>%
    group_by(age_group, shortName, agencyBonus) %>%
    summarize(meanAIC = mean(AIC))

# # Plot the results by age group 
AIC_age_plot <- ggplot(model_summary, aes(x = age_group, y = meanAIC, fill = shortName))+
    facet_wrap(~agencyBonus) +
    geom_bar(stat = "identity", position = "dodge", color = "black") +
    scale_fill_manual(name = "Model",
                      values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                      labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    coord_cartesian(ylim = c(350, 600)) +
    ylab("Mean AIC") +
    xlab("") +
    voc_theme() +
    theme(axis.text.x = element_text(angle = 60, hjust = 1))
AIC_age_plot
```

```{r aic overall plot, fig.width = 6, fig.height = 4, units = "in"}
model_summary_overall <- model_results %>%
    group_by(model, shortName, agencyBonus) %>%
    summarize(meanAIC = mean(AIC))

AIC_plot <- ggplot(model_summary_overall, aes(x = shortName, y = meanAIC, fill = shortName)) +
    geom_bar(stat = "identity", position = "dodge", color = "black") +
    facet_wrap(~agencyBonus) +
    coord_cartesian(ylim = c(350, 600)) + 
    ylab("Mean AIC") +
    xlab("Model") +
    scale_fill_manual(name = "Model",
                      values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                      labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    scale_x_discrete(labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    voc_theme() +
        theme(axis.text.x = element_text(angle = 75, hjust = 1),
              legend.position = "none")
AIC_plot

```

```{r aic overall difference plot, fig.width = 4, fig.height = 5, units = "in"}
#get minimum AIC
minAIC = min(model_summary_overall$meanAIC)

#subtract from mean AICs
model_difference_summary <- model_summary_overall %>%
    mutate(AIC_difference = meanAIC - minAIC[1]) %>%
    filter(agencyBonus == "With Agency Bonus")

#plot
AIC_difference_plot <- ggplot(model_difference_summary, aes(x = shortName, y = AIC_difference, fill = shortName)) +
    geom_bar(stat = "identity", position = "dodge", color = "black") +
    facet_wrap(~agencyBonus) +
    ylab("AIC Difference") +
    xlab("") +
    scale_fill_manual(name = "Model",
                      values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                      labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    scale_x_discrete(labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    voc_theme() +
        theme(axis.text.x = element_text(angle = 60, hjust = 1),
              legend.position = "none")
AIC_difference_plot

```

#  Examine age-related change in parameter estimates from models
```{r parameter estimates}

# load all parameters from each model
model_params <- read_csv("RL_modeling/output/model_fits_real_data/fourAlpha_twoBeta_agencyBonus.csv",
                         col_names = c("negLL",
                                       "logPost",
                                       "AIC",
                                       "BIC",
                                       "alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"))

#add sub ID and information
subID <- model_results %>% select(subID) %>% unique()
model_params <- bind_cols(subID, model_params)
model_params <- full_join(sub_info, model_params, by = c("subID"))
```


# Run regressions between model parameters and age
```{r param age regressions}

model_params$LL <- model_params$negLL * -1

# Log likelihood
summary(lm(LL ~ age, data = model_params))
# significant

# Alpha Pos Choice
summary(lm(alphaPosChoice ~ age, data = model_params))
#not significant

# Alpha Neg Choice
summary(lm(alphaNegChoice ~ age, data = model_params))
#not significant

# Alpha Pos Comp
summary(lm(alphaPosComp ~ age, data = model_params))
#not significant

# Alpha Neg Comp
summary(lm(alphaNegComp ~ age, data = model_params))
#not significant

# Beta Agency
summary(lm(betaAgency ~ age, data = model_params))
#significant

# Beta Bandit
summary(lm(betaMachine ~ age, data = model_params))
#not significant

# agency bonus
summary(lm(agencyBonus ~ age, data = model_params))
#not significant
```


# Learning rate model
```{r learning rate regression}
learning_rates <- model_params %>%
    pivot_longer(cols = c(alphaPosChoice:alphaNegComp),
                 names_to = "learningRate",
                 values_to = "estimate") %>%
    select(subID, age, age_group, learningRate, estimate) %>%
    unique() %>%
    mutate(valence = case_when(str_detect(learningRate, "Pos") ~ "Positive",
                               str_detect(learningRate, "Neg") ~ "Negative"),
           agency = case_when(str_detect(learningRate, "Choice") ~ "Choice",
                              str_detect(learningRate, "Comp") ~ "Comp"))
                               
learning_rates$ageZ <- scale_this(learning_rates$age)

learning_rate_model <- mixed(estimate ~ ageZ * valence * agency + (1|subID),
                             data = learning_rates,
                             method = "S")
learning_rate_model
summary(learning_rate_model)
# valence x agency interaction
# marginal valence x agency x age interaction

#t test between alpha pos choice and alpha neg choice
t.test(model_params$alphaPosChoice, model_params$alphaNegChoice, paired = T)
#significant

#t test between alpha pos comp and alpha neg comp
t.test(model_params$alphaPosComp, model_params$alphaNegComp, paired = T)
#not significant

```


# Plot relations between model parameters and age
```{r age parameter plot, fig.width = 7, fig.height = 4, units = "in"}

params_long <- model_params %>%
    pivot_longer(names_to = "param",
                 values_to = "estimate",
                 cols = c(alphaPosChoice:agencyBonus)) 

params_long$param <- factor(params_long$param, 
                            levels = c("alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"),
                            labels = c(TeX("$\\alpha_{choice_+}$"), 
                                       TeX("$\\alpha_{choice_-}$"), 
                                       TeX("$\\alpha_{comp_+}$"), 
                                       TeX("$\\alpha_{comp_-}$"), 
                                       TeX("$\\beta_{agency}$"), 
                                       TeX("$\\beta_{machine}$"),
                                       "Agency~Bonus"
                                ))

params_plot <- ggplot(params_long, aes(x = age, y = estimate, color = param)) +
    facet_wrap(~param, scale = "free", labeller = label_parsed, nrow = 2) +
    geom_point() +
    geom_smooth(method = "lm", aes(fill = param)) +
    ylab("Parameter Estimate") +
    xlab("Age") +
    voc_theme() +
    theme(legend.position = "none")
params_plot
```

# Parameter summary statistics
```{r parameter summary stats}

param_summary <- params_long %>%
    group_by(param) %>%
    summarize(meanEstimate = mean(estimate),
            seEstimate = sd(estimate)/sqrt(n()))
param_summary

```


# Questionnaire relations

## DOC
```{r doc}
# load questionnaire data
DOC <- read_csv("data/scored_surveys/DOC_scored.csv", col_names = TRUE) 

# merge with model params
DOC <- left_join(DOC, model_params)

# z score continuous variables
DOC$zAge <- scale_this(DOC$age)
DOC$zBetaAgency <- scale_this(DOC$betaAgency)
DOC$zAgencyBonus <- scale_this(DOC$agencyBonus)

# relation between DOC and age
lm(DOC ~ zAge, DOC) %>% summary()
#marginal positive effect (p = .058)

# relation between DOC and VoC
lm(DOC ~ zBetaAgency * zAgencyBonus *zAge, DOC) %>% summary()
# no effects

```

## LOC
```{r loc}
# load questionnaire data
LOC <- read_csv("data/scored_surveys/LOC_scored.csv", col_names = TRUE) 

# merge with model params
LOC <- left_join(LOC, model_params)

#z score continuous variables
LOC$zAge <- scale_this(DOC$age)
LOC$zBetaAgency <- scale_this(LOC$betaAgency)
LOC$zAgencyBonus <- scale_this(LOC$agencyBonus)

# relation between LOC and age
lm(LOC ~ zAge, LOC) %>% summary()
# no effect

# relation between LOC and VoC
lm(LOC ~ zBetaAgency * zAgencyBonus * zAge, LOC) %>% summary()
# no effects
```


## BDI
```{r bdi}
# load questionnaire data
BDI <- read_csv("data/scored_surveys/BDI_scored.csv", col_names = TRUE) 

# merge with model params
BDI <- left_join(BDI, model_params)

#z score continuous variables
BDI$zAge <- scale_this(BDI$age)
BDI$zBetaAgency <- scale_this(BDI$betaAgency)
BDI$zAgencyBonus <- scale_this(BDI$agencyBonus)

# relation between BDI and age
lm(zBDI ~ zAge, BDI) %>% summary()
# no effect

# relation between BDI and VoC 
lm(zBDI ~ zBetaAgency * zAgencyBonus *zAge, BDI) %>% summary()
# no effects

```


## STAI
```{r stai}
# load questionnaire data
STAI <- read_csv("data/scored_surveys/STAI_scored.csv", col_names = TRUE) 

# merge with model params
STAI <- left_join(STAI, model_params)

#z score continuous variables
STAI$zAge <- scale_this(STAI$age)
STAI$zBetaAgency <- scale_this(STAI$betaAgency)
STAI$zAgencyBonus <- scale_this(STAI$agencyBonus)

# relation between STAI_t and age
lm(zSTAI_t ~ zAge, STAI) %>% summary()
# no effect

# relation between STAI_t and VoC
lm(zSTAI_t  ~ zBetaAgency * zAgencyBonus *zAge, STAI) %>% summary()
# no effect

# relation between STAI_s and age
lm(zSTAI_s ~ zAge, STAI) %>% summary()
# no effects

# relation between STAI_s and VoC
lm(zSTAI_s  ~ zBetaAgency * zAgencyBonus *zAge, STAI) %>% summary()
# no effects
```